This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. Primary support for the subproject and the subproject's principal investigator may have been provided by other sources, including other NIH sources. The Total Cost listed for the subproject likely represents the estimated amount of Center infrastructure utilized by the subproject, not direct funding provided by the NCRR grant to the subproject or subproject staff. Introduction: Currently, the most reliable method of assessing pain is self-report. If however, self-report is unavailable or called into question, pain may be ignored or discredited. Therefore, a biomarker for pain would help to better inform clinical decisions. Suggesting that patterns of brain activity might provide a biomarker for pain, regions of the human brain are more activated by painful stimuli than by non-painful stimuli. This study aims to use fMRI to determine the degree to which patterns of neural activity can predict the presence or absence of pain. Methods: The protocol was approved by the Stanford Institutional Review Board. 16 and subjects provided written, informed consent and completed the experiment. Using fMRI, we recorded brain activity during the presentation of both painful and non-painful thermal stimuli. Using maps of brain activity from 8 study participants, we trained a linear support vector machine (SVM) to classify heat stimuli as painful or non-painful. The accuracy of this SVM was assessed by applying it to 8 novel study participants. To read about other projects ongoing at the Lucas Center, please visit http://rsl.stanford.edu/ (Lucas Annual Report and ISMRM 2011 Abstracts)